The genome undergoes extensive epigenetic remodeling over the course of life. In particular, DNA methylation at specific CpG sites has been consistently correlated with human age. This has led investigators to postulate an ?epigenetic clock?, that cells somehow encode the passage of time in DNA methylation and these patterns collectively reflect the chronological age and possibly the biological age and fitness of an individual. However, we currently know little about how these intriguing patterns in DNA methylation are established, and how these can influence health and disease during aging. While the general trends in age-related changes in DNA methylation are conserved across species, an equivalent method to measure the epigenetic age in model organisms such as the mouse has not yet been developed. Given the relevance to health and aging, defining such age signatures in mouse model will provide a powerful basis to study the mechanism and effects of these epigenetic changes, and provide a means to test agents and interventions that could modify (i.e., slow down or even reverse) the epigenetic clock. Here we propose to evaluate age-dependent changes in DNA methylation and gene expression in a cohort of recombinant inbred mouse strains that serves as a genetic reference panel for systems genetics. The BXDs are derived from the parental C57BL/6J and DBA/2J strains and have been widely used in research of aging and longevity. Members of the BXD family show significant variation in aging traits and lifespan. For this proposed study, we will select a set of short-lived and long-live strains for deep sequencing of the methylome and transcriptome at different ages. The specific aims of this proposal are to characterize age-dependent changes in DNA methylation (Aim 1) and gene expression (Aim 2) in BXD strains that are highly divergent in lifespan. We will examine if methylation and expression differences at a young age can differentiate between the short-lived and long-lived BXDs, and we will specifically test the hypothesis that time-dependent methylation patterns will show higher rates of change in strains with shorter average lifespan that indicate accelerated aging. We also hypothesize that multiple genes collectively contribute to the time-dependent methylation patterns and these modulators will form correlated transcriptional networks that also show age- dependent expression dynamics. Our ultimate goal is to define the mechanisms of age-dependent variation in DNA methylation, and identify epigenetic markers that are predictors of health and lifespan, and are potentially modifiable by lifestyle variables and interventions. This R21 will be the first step in establishing an experimental system to dissect the epigenetics and transcriptomics of aging and lifespan, and will subsequently be integrated to a larger multi- scale systems genetics study of aging.